{"entity":{"id":"idea-data-ai-post-market-performance-reporting","kind":"idea","name":"Mandatory post-market performance reporting for cancer AI","aka":[],"tldr":"Once an AI tool is in use, its maker and the hospital would have to report regularly how it is actually performing on real patients, and the reports would be public.","summary":"Post-market surveillance of medical devices focuses on adverse events, not performance. For AI, the relevant harm is silent degradation. The proposal requires deployed cancer AI to report standardised performance metrics (sensitivity, specificity, calibration, subgroup results, override rates) per site quarterly to the model registry, with thresholds that trigger regulator review, analogous to pharmacovigilance periodic safety update reports.","asOf":"2026-09-08","links":[{"label":"Bottleneck evidence (AI that is built but not validated or deployed): Wu et al., How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals (Nature Medicine 2021)","url":"https://doi.org/10.1038/s41591-021-01312-x"}],"tags":[],"related":["idea-data-clinical-ai-model-registry"],"cancers":[],"sections":["ai-computation"],"technologies":[],"targets":[],"drugs":[],"companies":[],"institutions":[],"pathways":[],"terms":[],"trials":[],"people":[],"bottlenecks":["b-ai-validation"],"keyPapers":["paper-wu-nat-med"],"journals":[],"dependsOn":[],"notes":[],"hypothesis":"Mandatory performance reporting will detect clinically significant performance degradation in a meaningful share of deployed models within their first two years, which would otherwise have gone unnoticed.","rationale":"Studies of deployed models (for example sepsis prediction tools) found real-world performance far below published claims, discovered only by independent academic audits; systematic reporting would make this routine.","test":"Require quarterly reports for all AI in one national screening programme for two years; count detected degradations and actions taken.","maturity":"speculative","actor":"regulator","cost":"medium","horizonYears":3},"route":"/ideas/idea-data-ai-post-market-performance-reporting/","neighbours":{"idea":[{"id":"idea-data-clinical-ai-model-registry","kind":"idea","name":"A public registry of every AI model used in cancer care","route":"/ideas/idea-data-clinical-ai-model-registry/"},{"id":"idea-data-drift-monitoring-standard","kind":"idea","name":"A standard for monitoring AI performance drift with pause thresholds","route":"/ideas/idea-data-drift-monitoring-standard/"},{"id":"idea-data-ai-audit-trail-in-ehr","kind":"idea","name":"Every AI output logged in the record with input hash, version and clinician response","route":"/ideas/idea-data-ai-audit-trail-in-ehr/"}],"section":[{"id":"ai-computation","kind":"section","name":"AI & Computation","route":"/fronts/ai-computation/"}],"bottleneck":[{"id":"b-ai-validation","kind":"bottleneck","name":"AI that is built but not validated or deployed","route":"/bottlenecks/b-ai-validation/"}],"paper":[{"id":"paper-wu-nat-med","kind":"paper","name":"How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals","route":"/key-papers/paper-wu-nat-med/"}]}}